class_docstr:     This is an auxiliary class that enables Video Streaming for ``caer`` with minimalistic latency, and at the expense
      of little to no additional computational requirements.
      
      The basic idea behind it is to tracks and save the salient feature array for the given number of frames and then uses these anchor point to cancel out all perturbations relative to it for the incoming frames in the queue. This class relies heavily on **Threaded Queue mode** for error-free & ultra-fast frame handling.
  
  Args:
      source (int, str): Source path for the video. Uses an external camera device if ``source`` is an integer.
      qsize (int): Default queue size for handling the video streams. Default: 128.
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docstr: Returns the fps (frames per second) value for the current video
  Optional: use the `fps` attribute
docstr_tok: ["Returns","the","fps","(","frames","per","second",")","value","for","the","current","video","Optional",":","use","the","`","fps","`","attribute"]
code: def get_fps(self) -> int:  # type: ignore[return]
      """
      Returns the fps (frames per second) value for the current video
      Optional: use the `fps` attribute
      """
      if not self.kill_stream:
          return self.fps
  
file_path: caer/caer/video/stream.py
imports: from threading import Thread
  import time
  import math
  from queue import Queue
  import cv2
  import numpy
  from .constants import FPS, FRAME_COUNT, FRAME_HEIGHT, FRAME_WIDTH
  from ..annotations import Tuple, Optional
  from ..coreten import Tensor
label_desc: Use image node_id 7 for calling the Stream obj's underlying member method code with example usage: obj.get_fps() and returns: self